Object tracking or matching is an important branch of the image processing field. During object tracking, the features of an object may be unstable. For example, the gray level and color features in a captured image may change due to the change of sun light radiation, and the texture feature of the image may be affected by the deformation of the object. A solution is to combine multiple of features. However, such a solution may incur a heavy computing load and may be hard to be implemented by hardware, and the like.
On the other hand, during object tracking, when initializing an object block, it may be unavoidable to include a part of background into the object block. Thus, interference may be introduced into features extracted from the object block. The larger the interference due to the background is, the lower the preciseness of subsequent object matching would be. Thus how to cancel the interference of the background is a hot point in object tracking technology. A solution is to use kernel function to decrease the interference from the background. The relevant documents include: Dorin Comaniciu, et al., “Kernel-based object tracking” (Pattern Analysis and Machine Intelligence, May, 2003) (referred to as Relevant Document 1). However, such a method cannot precisely cancel the interference of the background.